如何检查python中的一列的dtype ?

I need to use different functions to treat numeric columns and string columns. What I am doing now is really dumb:

我需要使用不同的函数来处理数字列和字符串列。我现在所做的是非常愚蠢的:

allc = list((agg.loc[:, (agg.dtypes==np.float64)|(agg.dtypes==np.int)]).columns)
for y in allc:
    treat_numeric(agg[y])    

allc = list((agg.loc[:, (agg.dtypes!=np.float64)&(agg.dtypes!=np.int)]).columns)
for y in allc:
    treat_str(agg[y])

Is there a more elegant way to do this? E.g.

有更优雅的方法吗?如。

for y in agg.columns:
    if(dtype(agg[y]) == 'string'):
          treat_str(agg[y])
    elif(dtype(agg[y]) != 'string'):
          treat_numeric(agg[y])

4 个解决方案

#1

You can access the data-type of a column with dtype:

可以使用dtype访问列的数据类型:

for y in agg.columns:
    if(agg[y].dtype == np.float64 or agg[y].dtype == np.int64):
          treat_numeric(agg[y])
    else:
          treat_str(agg[y])

#2

I know this is a bit of an old thread but with pandas 19.02, you can do:

我知道这有点老了，但对于熊猫19.02，你可以这样做:

df.select_dtypes(include=['float64']).apply(your_function)
df.select_dtypes(exclude=['string','object']).apply(your_other_function)

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.DataFrame.select_dtypes.html

#3

In pandas 0.20.2 you can do:

在熊猫0.20.2你可以做:

from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype

is_string_dtype(df['A'])
>>>> True

is_numeric_dtype(df['B'])
>>>> True

So your code becomes:

所以你的代码是:

for y in agg.columns:
    if (is_string_dtype(agg[y])):
        treat_str(agg[y])
    elif (is_numeric_dtype(agg[y])):
        treat_numeric(agg[y])

#4

If you want to mark the type of a dataframe column as a string, you can do:

如果要将dataframe列的类型标记为字符串，可以这样做:

df['A'].dtype.kind

An example:

一个例子:

In [8]: df = pd.DataFrame([[1,'a',1.2],[2,'b',2.3]])
In [9]: df[0].dtype.kind, df[1].dtype.kind, df[2].dtype.kind
Out[9]: ('i', 'O', 'f')

The answer for your code:

你的密码的答案是:

for y in agg.columns:
    if(agg[y].dtype.kind == 'f' or agg[y].dtype.kind == 'i'):
          treat_numeric(agg[y])
    else:
          treat_str(agg[y])

#1